Hands-On Reinforcement Learning with Python Free Download
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With significant enhancement in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art RL and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.
In addition to exploring reinforcement learning basics and foundational concepts like Bellman equation, Markov decision processes and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based and actor-critic RL methods. It explores in depth state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC, demystifying the underlying math and demonstrating implementations through simple code examples.
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